Exploring Diversity-Aware Augmented Learning for Multi-Solution Optimization

Machine learning has proven highly effective in addressing constrained optimization problems by approximating the mapping from hyperparameters to solutions. However, standard supervised learning methods often fall short due to the presence of multiple (sub-)optimal solutions. To address this challenge, we propose a diversity-aware augmented learning framework. Our approach transforms the one-to-many input-solution mapping into a function through the augmentation of the input space with initial points, thereby respecting the diversity of high-quality solutions. The proposed framework enhances the quality and diversity of optimal solution estimation, as evidenced by two case studies.